Abstract
The Internet of Things (IoT) has performed a paradigm shift in the method devices and systems interact, allowing seamless connectivity and communication. But, the enhancing interconnectedness and difficulty of IoT platforms also establish novel security problems, making intrusion detection a vital feature of IoT system security. Intrusion detection systems (IDS) role a vital play in detecting and monitoring unauthorized actions or malicious behavior in IoT networks. Typical IDS frequently depends on handcrafted rules or signatures that cannot not efficiently capture the difficult and developing nature of recent cyber threats. Deep learning (DL) methods automatically study and extract high-level representations in raw data, allowing more accurate and adaptive IDs. Most problems in emerging effectual IDSs are the presence of class imbalances in the database, but the count of normal instances far outweighs the count of intrusion samples. This paper focuses on the design of class imbalance data handling with optimal deep learning-based intrusion detection (CIDH-ODLID) techniques in IoT environments. The purpose of this study is to develop a CIDH-ODLID technique for the identification of intrusions in the IoT platform. For the class imbalance data handling process, the SMOTE approach is used in this work. In addition, the CIDH-ODLID technique employs an echo state network (ESN) approach for intrusion detection and classification. Finally, the snake optimization algorithm (SOA) was carried out for an optimum hyperparameter selection of the ESN approach. The performance validation of the CIDH-ODLID approach was performed on the benchmark database. To prove that the previously provided model performed better, a thorough simulation was run. The researchers provided a thorough comparative analysis that showed the proposed approach was better than other current procedures. It had an accuracy of 99.56%, precision of 97.50%, recall of 98.42%, and an F-score of 97.95%. The experimental outcomes exhibit the promising performance of the CIDH-ODLID technique over other models.
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References
Ahmad Z, Mahmood T, Rehman A, Saba T, Alamri FS (2023) Enhancing time series forecasting with an optimized binary gravitational search algorithm for echo state networks. IEEE Access 11:79466
Al Sawafi Y, Touzene A, Hedjam R (2023) Hybrid deep learning-based intrusion detection system for RPL IoT networks. J Sens Actuator Netw 12(2):21
Atul DJ, Kamalraj R, Ramesh G, Sankaran KS, Sharma S, Khasim S (2021) A machine learning based IoT for providing an intrusion detection system for security. Microprocess Microsyst 82:103741
Awajan A (2023) A novel deep learning-based intrusion detection system for IOT networks. Computers 12(2):34
Elsayed R, Hamada R, Hammoudeh M, Abdalla M, Elsaid SA (2022) A hierarchical deep learning-based intrusion detection architecture for clustered internet of things. J Sens Actuator Netw 12(1):3
Friha O, Ferrag MA, Shu L, Maglaras L, Choo KKR, Nafaa M (2022) FELIDS: federated learning-based intrusion detection system for agricultural Internet of Things. J Parall Distrib Comput 165:17–31
Fu X, Zhou N, Jiao L, Li H, Zhang J (2021) The robust deep learning–based schemes for intrusion detection in internet of things environments. Ann Telecommun 76(5–6):273–285
Hnamte V, Hussain J (2023) DCNNBiLSTM: an efficient hybrid deep learning-based intrusion detection system. Telemat Inf Rep 10:100053
Idrissi I, Azizi M, Moussaoui O (2022) A stratified IoT deep learning based intrusion detection system. In: 2022 2nd international conference on innovative research in applied science, engineering and technology (IRASET), pp 1–8. IEEE.
Islam N, Farhin F, Sultana I, Kaiser MS, Rahman MS, Mahmud M, SanwarHosen ASM, Cho GH (2021) Towards machine learning based intrusion detection in IoT networks. Comput Mater Continua 69(2):1801
Khan NW, Alshehri MS, Khan MA, Almakdi S, Moradpoor N, Alazeb A, Ullah S, Naz N, Ahmad J (2023) A hybrid deep learning-based intrusion detection system for IoT networks. Math Biosci Eng 20(8):13491–13520
Khurma RA, Albashish D, Braik M, Alzaqebah A, Qasem A, Adwan O (2023) An augmented Snake Optimizer for diseases and COVID-19 diagnosis. Biomed Signal Process Control 84:104718
Lin M, Zhao B, Xin Q (2020) ERID: a deep learning-based approach towards efficient real-time intrusion detection for IoT. In: 2020 IEEE eighth international conference on communications and networking (ComNet), pp 1–7. IEEE
Otoum Y, Liu D, Nayak A (2022) DL-IDS: a deep learning–based intrusion detection framework for securing IoT. Trans Emerg Telecommun Technol 33(3):e3803
Pampapathi BM, Guptha N, Hema MS (2022) Towards an effective deep learning-based intrusion detection system in the internet of things. Telem Inf Rep 7:100009
Rangelov D, Lämmel P, Brunzel L, Borgert S, Darius P, Tcholtchev N, Boerger M (2023) Towards an integrated methodology and toolchain for machine learning-based intrusion detection in Urban IoT networks and platforms. Future Internet 15(3):98
Ravi V, Chaganti R, Alazab M (2022) Deep learning feature fusion approach for an intrusion detection system in SDN-based IoT networks. IEEE Internet Things Mag 5(2):24–29
Rodríguez E, Valls P, Otero B, Costa JJ, Verdú J, Pajuelo MA, Canal R (2022) Transfer-learning-based intrusion detection framework in IoT networks. Sensors 22(15):5621
Shah H, Shah D, Jadav NK, Gupta R, Tanwar S, Alfarraj O, Tolba A, Raboaca MS, Marina V (2023) Deep learning-based malicious smart contract and intrusion detection system for IoT environment. Mathematics 11(2):418
Ullah I, Mahmoud QH (2020) A scheme for generating a dataset for anomalous activity detection in iot networks. In: Canadian conference on artificial intelligence, pp 508–520. Springer, Cham
Van Huong P, Hung DV (2019) Intrusion detection in IoT systems based on deep learning using convolutional neural network. In: 2019 6th NAFOSTED conference on information and computer science (NICS), pp 448–453. IEEE
Zakariah M, AlQahtani SA, Al-Rakhami MS (2023) Machine learning-based adaptive synthetic sampling technique for intrusion detection. Appl Sci 13:6504
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Srinivasan, M., Senthilkumar, N.C. Class imbalance data handling with optimal deep learning-based intrusion detection in IoT environment. Soft Comput 28, 4519–4529 (2024). https://doi.org/10.1007/s00500-023-09610-x
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DOI: https://doi.org/10.1007/s00500-023-09610-x